This paper investigates a blockage-aware anti-jamming strategy for UAV-assisted data harvesting in dense urban environments. Ground jammers (GJs) not only emit interference but also attempt to detect UAVs via line-of-sight (LoS) surveillance. To counter this, the UAV leverages urban building layouts to remain hidden from GJs and attenuate their jamming signals, while maintaining LoS connectivity with ground sensors (GSs) for secure and efficient data collection. To maximize the minimum average spectral efficiency among GSs, we jointly optimize the user scheduling and UAV trajectory based on a blockage-aware channel model that captures LoS/NLoS conditions induced by building layouts and relative node positions. This results in a challenging mixed-integer nonconvex problem due to binary scheduling variables, nonconvex trajectory constraints, and the use of sigmoid-approximated channel models. To tackle this, we decompose the original problem into two convex subproblems using successive convex approximation, quadratic transform, and geometry-based convexification techniques. An iterative block coordinate descent-based algorithm is then proposed, enabling efficient convergence with polynomial complexity. Simulation results confirm the superiority of the proposed scheme over baselines, particularly under severe jamming and building blockage conditions, demonstrating improved resilience and throughput in urban UAV networks.
This paper addresses the self-sustainable operation of unmanned aerial vehicle (UAV) swarms in dense urban 6G networks, where both communication reliability and energy replenishment are strongly affected by building-induced blockage. Although solar harvesting and laser wireless power transfer (WPT) can extend UAV operation, they are tightly coupled with UAV mobility: a communication-favorable position may not be feasible for laser charging, while a charging-oriented position may degrade ground node (GN) service. To capture this coupling, we develop a blockage-aware self-sustaining UAV swarm framework that integrates communication, solar harvesting, safety-compliant laser WPT, and UAV mobility under urban blockage. In the proposed model, buildings serve as common geometric constraints that determine both UAV–GN line-of-sight (LoS) connectivity and laser charging feasibility. We formulate a joint optimization problem of user association, UAV trajectory, laser charging decisions, and battery states to maximize the minimum spectral efficiency among GNs while ensuring sustainable energy operation. To address the resulting nonconvex mixed-integer nonlinear problem, we develop a tailored convexification framework for the coupled communication–charging–mobility design. Simulation results reveal that UAVs adapt their mobility according to solar availability: they prioritize short-range LoS communication when solar energy is sufficient, while moving toward safety-compliant and blockage-free laser charging regions under limited solar harvesting. These results highlight the need for joint mobility control that balances communication service and energy replenishment under building-induced blockage.
